Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations2200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory137.6 KiB
Average record size in memory64.1 B

Variable types

Numeric7
Categorical1

Alerts

K is highly overall correlated with labelHigh correlation
P is highly overall correlated with labelHigh correlation
humidity is highly overall correlated with labelHigh correlation
label is highly overall correlated with K and 3 other fieldsHigh correlation
rainfall is highly overall correlated with labelHigh correlation
label is uniformly distributed Uniform
temperature has unique values Unique
humidity has unique values Unique
ph has unique values Unique
rainfall has unique values Unique
N has 27 (1.2%) zeros Zeros

Reproduction

Analysis started2025-01-06 02:04:26.993646
Analysis finished2025-01-06 02:04:37.483585
Duration10.49 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

N
Real number (ℝ)

Zeros 

Distinct137
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.551818
Minimum0
Maximum140
Zeros27
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-01-06T11:04:37.628502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q121
median37
Q384.25
95-th percentile116
Maximum140
Range140
Interquartile range (IQR)63.25

Descriptive statistics

Standard deviation36.917334
Coefficient of variation (CV)0.73028696
Kurtosis-1.0582399
Mean50.551818
Median Absolute Deviation (MAD)26
Skewness0.50972137
Sum111214
Variance1362.8895
MonotonicityNot monotonic
2025-01-06T11:04:37.840390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 44
 
2.0%
40 44
 
2.0%
27 42
 
1.9%
39 41
 
1.9%
31 41
 
1.9%
32 39
 
1.8%
37 39
 
1.8%
34 38
 
1.7%
29 37
 
1.7%
36 35
 
1.6%
Other values (127) 1800
81.8%
ValueCountFrequency (%)
0 27
1.2%
1 20
0.9%
2 26
1.2%
3 21
1.0%
4 27
1.2%
5 27
1.2%
6 29
1.3%
7 25
1.1%
8 29
1.3%
9 33
1.5%
ValueCountFrequency (%)
140 3
0.1%
139 1
 
< 0.1%
136 2
 
0.1%
135 1
 
< 0.1%
134 2
 
0.1%
133 4
0.2%
132 2
 
0.1%
131 6
0.3%
130 1
 
< 0.1%
129 3
0.1%

P
Real number (ℝ)

High correlation 

Distinct117
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.362727
Minimum5
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-01-06T11:04:38.047330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q128
median51
Q368
95-th percentile133
Maximum145
Range140
Interquartile range (IQR)40

Descriptive statistics

Standard deviation32.985883
Coefficient of variation (CV)0.61814462
Kurtosis0.86027876
Mean53.362727
Median Absolute Deviation (MAD)20
Skewness1.0107725
Sum117398
Variance1088.0685
MonotonicityNot monotonic
2025-01-06T11:04:38.262506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 56
 
2.5%
58 48
 
2.2%
56 46
 
2.1%
55 44
 
2.0%
57 42
 
1.9%
59 41
 
1.9%
18 39
 
1.8%
21 39
 
1.8%
25 37
 
1.7%
40 37
 
1.7%
Other values (107) 1771
80.5%
ValueCountFrequency (%)
5 22
1.0%
6 24
1.1%
7 25
1.1%
8 20
0.9%
9 17
0.8%
10 14
0.6%
11 21
1.0%
12 17
0.8%
13 15
0.7%
14 19
0.9%
ValueCountFrequency (%)
145 8
0.4%
144 12
0.5%
143 10
0.5%
142 7
0.3%
141 7
0.3%
140 12
0.5%
139 12
0.5%
138 10
0.5%
137 7
0.3%
136 10
0.5%

K
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.149091
Minimum5
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-01-06T11:04:38.516400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile15
Q120
median32
Q349
95-th percentile199
Maximum205
Range200
Interquartile range (IQR)29

Descriptive statistics

Standard deviation50.647931
Coefficient of variation (CV)1.051898
Kurtosis4.4493544
Mean48.149091
Median Absolute Deviation (MAD)13
Skewness2.3751672
Sum105928
Variance2565.2129
MonotonicityNot monotonic
2025-01-06T11:04:38.808315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 90
 
4.1%
22 87
 
4.0%
15 86
 
3.9%
20 80
 
3.6%
25 78
 
3.5%
19 77
 
3.5%
21 74
 
3.4%
18 72
 
3.3%
45 65
 
3.0%
23 63
 
2.9%
Other values (63) 1428
64.9%
ValueCountFrequency (%)
5 8
0.4%
6 9
0.4%
7 5
0.2%
8 12
0.5%
9 12
0.5%
10 12
0.5%
11 8
0.4%
12 9
0.4%
13 7
0.3%
14 9
0.4%
ValueCountFrequency (%)
205 18
0.8%
204 22
1.0%
203 22
1.0%
202 14
0.6%
201 18
0.8%
200 14
0.6%
199 14
0.6%
198 15
0.7%
197 24
1.1%
196 21
1.0%

temperature
Real number (ℝ)

Unique 

Distinct2200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.616244
Minimum8.8256747
Maximum43.675493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-01-06T11:04:39.022253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8.8256747
5-th percentile17.915085
Q122.769375
median25.598693
Q328.561654
95-th percentile34.056636
Maximum43.675493
Range34.849818
Interquartile range (IQR)5.7922793

Descriptive statistics

Standard deviation5.0637486
Coefficient of variation (CV)0.19767725
Kurtosis1.2325549
Mean25.616244
Median Absolute Deviation (MAD)2.9015036
Skewness0.18493273
Sum56355.736
Variance25.64155
MonotonicityNot monotonic
2025-01-06T11:04:39.248173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.87974371 1
 
< 0.1%
29.48069921 1
 
< 0.1%
29.94349168 1
 
< 0.1%
28.03306461 1
 
< 0.1%
29.8843055 1
 
< 0.1%
27.7058373 1
 
< 0.1%
29.78714005 1
 
< 0.1%
28.57819995 1
 
< 0.1%
27.51492243 1
 
< 0.1%
27.72653142 1
 
< 0.1%
Other values (2190) 2190
99.5%
ValueCountFrequency (%)
8.825674745 1
< 0.1%
9.467960445 1
< 0.1%
9.535585543 1
< 0.1%
9.724457611 1
< 0.1%
9.851242629 1
< 0.1%
9.949929082 1
< 0.1%
10.01081312 1
< 0.1%
10.16431299 1
< 0.1%
10.2708877 1
< 0.1%
10.35609594 1
< 0.1%
ValueCountFrequency (%)
43.67549305 1
< 0.1%
43.36051537 1
< 0.1%
43.30204933 1
< 0.1%
43.08022702 1
< 0.1%
43.03714283 1
< 0.1%
42.93605359 1
< 0.1%
42.93368602 1
< 0.1%
42.92325255 1
< 0.1%
42.84609252 1
< 0.1%
42.54744013 1
< 0.1%

humidity
Real number (ℝ)

High correlation  Unique 

Distinct2200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.481779
Minimum14.25804
Maximum99.981876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-01-06T11:04:39.478111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum14.25804
5-th percentile19.374917
Q160.261953
median80.473146
Q389.948771
95-th percentile94.368844
Maximum99.981876
Range85.723836
Interquartile range (IQR)29.686818

Descriptive statistics

Standard deviation22.263812
Coefficient of variation (CV)0.31146135
Kurtosis0.30213407
Mean71.481779
Median Absolute Deviation (MAD)12.103236
Skewness-1.0917079
Sum157259.91
Variance495.67731
MonotonicityNot monotonic
2025-01-06T11:04:39.710359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.00274423 1
 
< 0.1%
90.33698678 1
 
< 0.1%
93.90741192 1
 
< 0.1%
91.47355778 1
 
< 0.1%
94.0371147 1
 
< 0.1%
92.91185695 1
 
< 0.1%
94.65343534 1
 
< 0.1%
92.86597437 1
 
< 0.1%
94.96218673 1
 
< 0.1%
92.00687531 1
 
< 0.1%
Other values (2190) 2190
99.5%
ValueCountFrequency (%)
14.25803981 1
< 0.1%
14.27327988 1
< 0.1%
14.2804191 1
< 0.1%
14.32313811 1
< 0.1%
14.33847406 1
< 0.1%
14.42457525 1
< 0.1%
14.44008871 1
< 0.1%
14.44228303 1
< 0.1%
14.69765308 1
< 0.1%
14.70085967 1
< 0.1%
ValueCountFrequency (%)
99.98187601 1
< 0.1%
99.96906006 1
< 0.1%
99.84671638 1
< 0.1%
99.7240104 1
< 0.1%
99.65809151 1
< 0.1%
99.64573002 1
< 0.1%
99.64328526 1
< 0.1%
99.34854917 1
< 0.1%
99.18843684 1
< 0.1%
98.80313612 1
< 0.1%

ph
Real number (ℝ)

Unique 

Distinct2200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4694801
Minimum3.5047523
Maximum9.9350907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-01-06T11:04:39.927230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3.5047523
5-th percentile5.4351117
Q15.9716928
median6.4250453
Q36.9236426
95-th percentile7.7484174
Maximum9.9350907
Range6.4303384
Interquartile range (IQR)0.95194982

Descriptive statistics

Standard deviation0.77393769
Coefficient of variation (CV)0.11962904
Kurtosis1.6555815
Mean6.4694801
Median Absolute Deviation (MAD)0.4743912
Skewness0.28392944
Sum14232.856
Variance0.59897954
MonotonicityNot monotonic
2025-01-06T11:04:40.430218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.502985292 1
 
< 0.1%
6.640470863 1
 
< 0.1%
6.251420275 1
 
< 0.1%
6.274452811 1
 
< 0.1%
6.135996372 1
 
< 0.1%
6.194090172 1
 
< 0.1%
6.327822962 1
 
< 0.1%
6.212567211 1
 
< 0.1%
6.685553129 1
 
< 0.1%
6.350623739 1
 
< 0.1%
Other values (2190) 2190
99.5%
ValueCountFrequency (%)
3.504752314 1
< 0.1%
3.510404312 1
< 0.1%
3.5253661 1
< 0.1%
3.532008668 1
< 0.1%
3.558822825 1
< 0.1%
3.692863601 1
< 0.1%
3.71105919 1
< 0.1%
3.793575185 1
< 0.1%
3.808429173 1
< 0.1%
3.828031463 1
< 0.1%
ValueCountFrequency (%)
9.93509073 1
< 0.1%
9.926212291 1
< 0.1%
9.679240873 1
< 0.1%
9.45949344 1
< 0.1%
9.416003106 1
< 0.1%
9.406887533 1
< 0.1%
9.392694614 1
< 0.1%
9.254089438 1
< 0.1%
9.160691747 1
< 0.1%
9.112771682 1
< 0.1%

rainfall
Real number (ℝ)

High correlation  Unique 

Distinct2200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.46366
Minimum20.211267
Maximum298.56012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-01-06T11:04:40.651299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20.211267
5-th percentile33.823512
Q164.551686
median94.867624
Q3124.26751
95-th percentile209.54244
Maximum298.56012
Range278.34885
Interquartile range (IQR)59.715822

Descriptive statistics

Standard deviation54.958389
Coefficient of variation (CV)0.53118545
Kurtosis0.60707929
Mean103.46366
Median Absolute Deviation (MAD)30.103324
Skewness0.96575635
Sum227620.04
Variance3020.4245
MonotonicityNot monotonic
2025-01-06T11:04:40.856972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202.9355362 1
 
< 0.1%
26.0365768 1
 
< 0.1%
20.39020503 1
 
< 0.1%
21.17924769 1
 
< 0.1%
21.0000988 1
 
< 0.1%
22.06207161 1
 
< 0.1%
27.8659442 1
 
< 0.1%
27.5987178 1
 
< 0.1%
21.01796432 1
 
< 0.1%
20.21126747 1
 
< 0.1%
Other values (2190) 2190
99.5%
ValueCountFrequency (%)
20.21126747 1
< 0.1%
20.36001144 1
< 0.1%
20.39020503 1
< 0.1%
20.49035619 1
< 0.1%
20.66127836 1
< 0.1%
20.76212031 1
< 0.1%
20.76223014 1
< 0.1%
20.76582087 1
< 0.1%
20.88620369 1
< 0.1%
21.0000988 1
< 0.1%
ValueCountFrequency (%)
298.5601175 1
< 0.1%
298.4018471 1
< 0.1%
295.9248796 1
< 0.1%
295.6094492 1
< 0.1%
291.2986618 1
< 0.1%
290.6793783 1
< 0.1%
287.5766935 1
< 0.1%
286.5083725 1
< 0.1%
285.2493645 1
< 0.1%
284.4364567 1
< 0.1%

label
Categorical

High correlation  Uniform 

Distinct22
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.3 KiB
rice
 
100
maize
 
100
chickpea
 
100
kidneybeans
 
100
pigeonpeas
 
100
Other values (17)
1700 

Length

Max length11
Median length9
Mean length7.1363636
Min length4

Characters and Unicode

Total characters15700
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrice
2nd rowrice
3rd rowrice
4th rowrice
5th rowrice

Common Values

ValueCountFrequency (%)
rice 100
 
4.5%
maize 100
 
4.5%
chickpea 100
 
4.5%
kidneybeans 100
 
4.5%
pigeonpeas 100
 
4.5%
mothbeans 100
 
4.5%
mungbean 100
 
4.5%
blackgram 100
 
4.5%
lentil 100
 
4.5%
pomegranate 100
 
4.5%
Other values (12) 1200
54.5%

Length

2025-01-06T11:04:41.111833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rice 100
 
4.5%
maize 100
 
4.5%
jute 100
 
4.5%
cotton 100
 
4.5%
coconut 100
 
4.5%
papaya 100
 
4.5%
orange 100
 
4.5%
apple 100
 
4.5%
muskmelon 100
 
4.5%
watermelon 100
 
4.5%
Other values (12) 1200
54.5%

Most occurring characters

ValueCountFrequency (%)
e 2100
13.4%
a 2100
13.4%
n 1600
 
10.2%
o 1200
 
7.6%
m 900
 
5.7%
p 900
 
5.7%
c 800
 
5.1%
t 800
 
5.1%
g 700
 
4.5%
i 600
 
3.8%
Other values (13) 4000
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2100
13.4%
a 2100
13.4%
n 1600
 
10.2%
o 1200
 
7.6%
m 900
 
5.7%
p 900
 
5.7%
c 800
 
5.1%
t 800
 
5.1%
g 700
 
4.5%
i 600
 
3.8%
Other values (13) 4000
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2100
13.4%
a 2100
13.4%
n 1600
 
10.2%
o 1200
 
7.6%
m 900
 
5.7%
p 900
 
5.7%
c 800
 
5.1%
t 800
 
5.1%
g 700
 
4.5%
i 600
 
3.8%
Other values (13) 4000
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2100
13.4%
a 2100
13.4%
n 1600
 
10.2%
o 1200
 
7.6%
m 900
 
5.7%
p 900
 
5.7%
c 800
 
5.1%
t 800
 
5.1%
g 700
 
4.5%
i 600
 
3.8%
Other values (13) 4000
25.5%

Interactions

2025-01-06T11:04:35.980124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:27.305360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:28.803876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:30.596619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:31.897866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:33.567384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:34.860675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:36.214990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:27.587190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:28.917812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:30.819491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:32.200706image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:33.697336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:35.011615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:36.448856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:27.788075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:29.034744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:30.986387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:32.786409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:33.827225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:35.153544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:36.611763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:27.987961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:29.189673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:31.121309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:32.950316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:34.350931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:35.300476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:36.773673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:28.231822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:29.403550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:31.299209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:33.140206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:34.481855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:35.451390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:36.893602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:28.439702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:29.768393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:31.421138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:33.265557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:34.602787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:35.579353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:37.022528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:28.616984image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:30.442685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:31.680000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:33.436460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:34.726739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T11:04:35.774242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-06T11:04:41.239170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
KNPhumiditylabelphrainfalltemperature
K1.0000.2080.1950.2790.969-0.1550.070-0.081
N0.2081.000-0.1630.0610.4700.1420.0110.022
P0.195-0.1631.000-0.3090.598-0.119-0.032-0.138
humidity0.2790.061-0.3091.0000.653-0.0070.1010.124
label0.9690.4700.5980.6531.0000.3780.5670.432
ph-0.1550.142-0.119-0.0070.3781.000-0.1510.028
rainfall0.0700.011-0.0320.1010.567-0.1511.000-0.152
temperature-0.0810.022-0.1380.1240.4320.028-0.1521.000

Missing values

2025-01-06T11:04:37.214419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-06T11:04:37.407843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NPKtemperaturehumidityphrainfalllabel
090424320.87974482.0027446.502985202.935536rice
185584121.77046280.3196447.038096226.655537rice
260554423.00445982.3207637.840207263.964248rice
374354026.49109680.1583636.980401242.864034rice
478424220.13017581.6048737.628473262.717340rice
569374223.05804983.3701187.073454251.055000rice
669553822.70883882.6394145.700806271.324860rice
794534020.27774482.8940865.718627241.974195rice
889543824.51588183.5352166.685346230.446236rice
968583823.22397483.0332276.336254221.209196rice
NPKtemperaturehumidityphrainfalllabel
2190103403027.30901855.1962246.348316141.483164coffee
2191118313427.54823062.8817926.123796181.417081coffee
2192106213525.62735557.0415117.428524188.550654coffee
2193116383423.29250350.0455706.020947183.468585coffee
219497352624.91461053.7414476.334610166.254931coffee
2195107343226.77463766.4132696.780064177.774507coffee
219699152727.41711256.6363626.086922127.924610coffee
2197118333024.13179767.2251236.362608173.322839coffee
2198117323426.27241852.1273946.758793127.175293coffee
2199104183023.60301660.3964756.779833140.937041coffee